Intelligent Decision-Making System for Driver Assistance Under Crosswinds Based on Multi-Task Supervised Learning

Intelligent Decision-Making System for Driver Assistance Under Crosswinds Based on Multi-Task Supervised Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Intelligent Decision-Making System for Driver Assistance Under Crosswinds Based on Multi-Task Supervised Learning Haoli Feng, Yun Li, Shutin Zhan, Rui Wu, Yuxuan Gao, Ruicheng Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9189740/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Improving the stability and safety of vehicles is the core goal of intelligent driving. This study aims to study the decision-making method of vehicle assistant driv- ing under cross-wind conditions through multi-task supervised learning, in order to improve the handling and safety of vehicles in complex wind environments. According to the data acquisition of wind speed and wind direction in the cross- wind frequent area, the influence of wind speed and wind speed on the vehicle was simulated based on the CarSim and Python co-simulation environment, and the multi-task supervised learning method was used to analyze the influence of crosswind wind speed, wind direction and vehicle speed on the vehicle deflection Angle. An assistant driving decision-making scheme based on real-time data is designed, which realizes high-precision real-time perception and decision-making through multi-task supervised learning and vehicle crosswind detection sensors. It is found that under the wind speed environment of 9.30-24.70m/s, the deflec- tion Angle of the car tire ranges from 0.025。to 0.2。. The higher the speed, the greater the deflection Angle. The deviation between the wind direction and the driving direction has a significant impact on the deflection Angle, and the car deflection realizes auxiliary correction accordingly. It can accurately identify the wind speed and direction, make reasonable driving decisions, and significantly improve the safety and maneuverability of vehicles in complex cross wind envi- ronment. More research is needed to further optimize the model and add actual driving feedback data to improve the adaptability and accuracy of the system. CarSim co-simulation cross wind monitoring intelligent decision-making multi-task supervised learning Traffic safety Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9189740","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610358991,"identity":"fc11c719-42f9-4205-9cbb-c56742e46719","order_by":0,"name":"Haoli Feng","email":"","orcid":"","institution":"Guangdong Polytechnic Normal University","correspondingAuthor":false,"prefix":"","firstName":"Haoli","middleName":"","lastName":"Feng","suffix":""},{"id":610358992,"identity":"c64d61f1-5f12-4cf4-b91b-479152055936","order_by":1,"name":"Yun 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Traffic safety","lastPublishedDoi":"10.21203/rs.3.rs-9189740/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9189740/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Improving the stability and safety of vehicles is the core goal of intelligent driving.\nThis study aims to study the decision-making method of vehicle assistant driv- ing under cross-wind conditions through multi-task supervised learning, in order to improve the handling and safety of vehicles in complex wind environments.\nAccording to the data acquisition of wind speed and wind direction in the cross- wind frequent area, the influence of wind speed and wind speed on the vehicle was simulated based on the CarSim and Python co-simulation environment, and the multi-task supervised learning method was used to analyze the influence of crosswind wind speed, wind direction and vehicle speed on the vehicle deflection Angle. An assistant driving decision-making scheme based on real-time data is designed, which realizes high-precision real-time perception and decision-making through multi-task supervised learning and vehicle crosswind detection sensors.\nIt is found that under the wind speed environment of 9.30-24.70m/s, the deflec- tion Angle of the car tire ranges from 0.025。to 0.2。. The higher the speed, the greater the deflection Angle. The deviation between the wind direction and the driving direction has a significant impact on the deflection Angle, and the car deflection realizes auxiliary correction accordingly. It can accurately identify the wind speed and direction, make reasonable driving decisions, and significantly improve the safety and maneuverability of vehicles in complex cross wind envi- ronment. 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